Unconditional Image Generation
Diffusers
Safetensors
English
afm
adversarial-flow-models
class-conditional
imagenet
Instructions to use BiliSakura/AFM-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BiliSakura/AFM-diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("BiliSakura/AFM-diffusers", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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README.md
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---
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license: mit
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library_name: diffusers
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pipeline_tag:
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tags:
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- diffusers
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- afm
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).to("cuda")
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image = pipe(class_labels="golden retriever", num_inference_steps=1).images[0]
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```
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---
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license: mit
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library_name: diffusers
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pipeline_tag: unconditional-image-generation
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tags:
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- diffusers
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- afm
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).to("cuda")
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image = pipe(class_labels="golden retriever", num_inference_steps=1).images[0]
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```
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